Improve SillyHome discovery and feedback learning

CHANGELOG.md

@@ -1,5 +1,14 @@
 # Changelog
 
+## 0.7.11 - 2026-06-16
+- Aktor-Discovery erkennt weitere steuerbare HA-Domains wie Buttons, Helper,
+  Heizungen, Schlösser, Ventile und numerische Helper.
+- Aktor-Auswahl dedupliziert Licht-/Schalter-Doppelungen pro Gerät und gruppiert
+  zusätzliche Typen im Dashboard.
+- Discovery liefert Kategorien für Mess-, Binär-, Kontext- und Aktor-Entities.
+- Nutzerfeedback kann Vorhersagen als korrekt oder falsch markieren und direkt
+  als Lernsignal speichern.
+
 ## 0.7.10 - 2026-06-16
 - WebSocket-State-Changes aktualisieren einen internen Home-Assistant-State-
   Cache und werten Aktoren direkt gegen diesen frischen Event-Zustand aus.

addon/config.yaml

@@ -1,5 +1,5 @@
 name: SillyHome Next
-version: "0.7.10"
+version: "0.7.11"
 slug: sillyhome_next
 description: Lernt automatisch aus deinem Verhalten und steuert freigegebene Aktoren
 url: http://192.168.6.31:3000/pino/sillyhome-next

app/api/v1/actuators.py

@@ -38,12 +38,22 @@ class ManualAssignmentRequest(BaseModel):
     note: str | None = Field(default=None, max_length=500)
 
 
+class FeedbackRequest(BaseModel):
+    correct: bool
+    expected_state: str | None = Field(default=None, max_length=100)
+
+
 @router.get("/discovery", response_model=list[HaEntitySummary])
 def discover_actuators(ha_reader: HaReader = Depends(get_ha_reader)) -> list[HaEntitySummary]:
     entities = {entity.entity_id: entity for entity in ha_reader.read_entities()}
     discovered = ha_reader.discover()
-    actuator_ids = sorted(
-        entity.entity_id for entity in discovered if entity.role is EntityRole.ACTUATOR
+    actuator_ids = _deduplicate_actuator_ids(
+        [
+            (entity.entity_id, entity.category)
+            for entity in discovered
+            if entity.role is EntityRole.ACTUATOR
+        ],
+        entities,
     )
     return [entities[entity_id] for entity_id in actuator_ids if entity_id in entities]
 
@@ -116,6 +126,22 @@ def evaluate_actuator(
         raise HTTPException(status_code=404, detail=str(exc)) from exc
 
 
+@router.post("/{actuator_entity_id}/feedback", response_model=ActuatorRecord)
+def record_feedback(
+    actuator_entity_id: str,
+    payload: FeedbackRequest,
+    request: Request,
+) -> ActuatorRecord:
+    try:
+        return _behavior(request).record_feedback(
+            actuator_entity_id,
+            correct=payload.correct,
+            expected_state=payload.expected_state,
+        )
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+
+
 @router.post("/{actuator_entity_id}/activation", response_model=ActuatorRecord)
 def set_activation(
     actuator_entity_id: str,
@@ -233,3 +259,38 @@ def _behavior(request: Request) -> BehaviorEngine:
             detail="Verhaltenslernen ist nicht initialisiert.",
         )
     return engine
+
+
+def _deduplicate_actuator_ids(
+    discovered: list[tuple[str, str]],
+    entities: dict[str, HaEntitySummary],
+) -> list[str]:
+    priority = {
+        "light": 0,
+        "cover_shutter": 1,
+        "heating": 2,
+        "lock": 3,
+        "fan": 4,
+        "switch_socket": 5,
+        "button": 6,
+        "helper": 7,
+    }
+    selected: dict[str, tuple[int, str]] = {}
+    for entity_id, category in discovered:
+        entity = entities.get(entity_id)
+        if entity is None:
+            continue
+        key = _actuator_duplicate_key(entity, category)
+        rank = priority.get(category, 50)
+        current = selected.get(key)
+        if current is None or (rank, entity_id) < current:
+            selected[key] = (rank, entity_id)
+    return sorted(entity_id for _, entity_id in selected.values())
+
+
+def _actuator_duplicate_key(entity: HaEntitySummary, category: str) -> str:
+    if entity.device_id and category in {"light", "switch_socket", "button"}:
+        return f"device:{entity.device_id}:control"
+    if entity.device_name and category in {"light", "switch_socket", "button"}:
+        return f"device-name:{entity.device_name.lower()}:control"
+    return f"entity:{entity.entity_id}"

app/behavior/engine.py

@@ -356,6 +356,95 @@ class BehaviorEngine:
                 )
         return self._save_behavior(record, behavior)
 
+    def record_feedback(
+        self,
+        actuator_entity_id: str,
+        *,
+        correct: bool,
+        expected_state: str | None = None,
+    ) -> ActuatorRecord:
+        record = self._store.get(actuator_entity_id)
+        now = datetime.now(timezone.utc)
+        entities = {entity.entity_id: entity for entity in self._ha_reader.read_entities()}
+        actuator = entities.get(actuator_entity_id)
+        if actuator is None:
+            raise KeyError("Aktor ist aktuell nicht in Home Assistant verfügbar.")
+        context_ids = [
+            entity_id
+            for entity_id in [
+                record.assignment.selected_numeric_entity_id,
+                *record.assignment.selected_context_entity_ids,
+            ]
+            if entity_id
+        ]
+        current_context = {
+            entity_id: entities[entity_id].state
+            for entity_id in context_ids
+            if entity_id in entities and entities[entity_id].state is not None
+        }
+        prediction = record.behavior.prediction
+        patterns = list(record.behavior.patterns)
+        reason = "Nutzerfeedback gespeichert."
+        if correct and prediction is not None:
+            local = now.astimezone(ZoneInfo(self._settings.timezone))
+            patterns.append(
+                BehaviorPattern(
+                    target_state=prediction.target_state,
+                    minute_of_day=local.hour * 60 + local.minute,
+                    weekday=local.weekday(),
+                    context_states={
+                        entity_id: state
+                        for entity_id, state in current_context.items()
+                        if state is not None
+                    },
+                    source="user_feedback",
+                    weight=1.0,
+                    observed_at=now,
+                )
+            )
+            reason = "Vorhersage wurde vom Nutzer als korrekt bestätigt."
+        else:
+            target = prediction.target_state if prediction is not None else None
+            if target:
+                patterns = [
+                    pattern.model_copy(update={"weight": 0.1})
+                    if pattern.target_state == target
+                    and _pattern_context_matches(pattern, current_context)
+                    else pattern
+                    for pattern in patterns
+                ]
+            if expected_state:
+                local = now.astimezone(ZoneInfo(self._settings.timezone))
+                patterns.append(
+                    BehaviorPattern(
+                        target_state=expected_state,
+                        minute_of_day=local.hour * 60 + local.minute,
+                        weekday=local.weekday(),
+                        context_states={
+                            entity_id: state
+                            for entity_id, state in current_context.items()
+                            if state is not None
+                        },
+                        source="user_correction",
+                        weight=1.0,
+                        observed_at=now,
+                    )
+                )
+            reason = "Vorhersage wurde vom Nutzer als falsch markiert."
+        behavior = record.behavior.model_copy(
+            update={
+                "patterns": patterns[-_MAX_PATTERNS:],
+                "prediction": (
+                    prediction.model_copy(update={"execution_reason": reason})
+                    if prediction is not None
+                    else None
+                ),
+                "reason": reason,
+                "last_trained_at": now,
+            }
+        )
+        return self._save_behavior(record, behavior)
+
     def refresh_related_automations(self, actuator_entity_id: str) -> ActuatorRecord:
         record = self._store.get(actuator_entity_id)
         related = [
@@ -856,6 +945,20 @@ def _matches_own_execution(
     )
 
 
+def _pattern_context_matches(
+    pattern: BehaviorPattern,
+    current_context: dict[str, str | None],
+) -> bool:
+    comparable = [
+        (entity_id, expected)
+        for entity_id, expected in pattern.context_states.items()
+        if entity_id in current_context
+    ]
+    if not comparable:
+        return False
+    return all(current_context[entity_id] == expected for entity_id, expected in comparable)
+
+
 def _recent_context_transition(
     history: dict[str, StateHistorySeries],
     context_ids: list[str],

app/ha/discovery.py

@@ -21,6 +21,7 @@ class DiscoveredEntity(BaseModel):
     device_class: str | None = None
     state_class: str | None = None
     unit_of_measurement: str | None = None
+    category: str
     role: EntityRole
     learnable: bool
     reason: str
@@ -82,14 +83,39 @@ _BINARY_CONTEXT_CLASSES = frozenset({
     "window",
 })
 _ACTUATOR_DOMAINS = frozenset({
+    "button",
+    "climate",
     "cover",
     "fan",
     "humidifier",
+    "input_boolean",
+    "input_button",
+    "lock",
     "light",
+    "number",
+    "siren",
     "switch",
+    "valve",
+})
+_CONTEXT_DOMAINS = frozenset({
+    "device_tracker",
+    "input_boolean",
+    "input_datetime",
+    "input_number",
+    "input_select",
+    "person",
+    "sun",
+    "weather",
+    "zone",
+})
+_LEARNABLE_CONTEXT_DOMAINS = frozenset({
+    "device_tracker",
+    "input_boolean",
+    "input_number",
+    "input_select",
+    "person",
+    "weather",
 })
-_CONTEXT_DOMAINS = frozenset({"device_tracker", "person", "sun", "weather", "zone"})
-_LEARNABLE_CONTEXT_DOMAINS = frozenset({"device_tracker", "person", "weather"})
 _NUMERIC_STATE_CLASSES = frozenset({"measurement", "total", "total_increasing"})
 
 
@@ -102,6 +128,7 @@ def classify_entity(entity: HaEntitySummary) -> DiscoveredEntity:
         return _result(
             entity,
             EntityRole.MEASUREMENT,
+            category=_measurement_category(entity),
             learnable=True,
             reason="Numerischer Messsensor für Zeitreihen und Training.",
         )
@@ -110,6 +137,7 @@ def classify_entity(entity: HaEntitySummary) -> DiscoveredEntity:
         return _result(
             entity,
             EntityRole.BINARY_CONTEXT,
+            category=_binary_category(entity),
             learnable=True,
             reason="Binärer Kontextsensor für Zustands- und Anwesenheitsmuster.",
         )
@@ -119,6 +147,7 @@ def classify_entity(entity: HaEntitySummary) -> DiscoveredEntity:
         return _result(
             entity,
             EntityRole.CONTEXT,
+            category=_context_category(entity),
             learnable=learnable,
             reason=(
                 "Kontextquelle für Training und Erklärungen."
@@ -131,6 +160,7 @@ def classify_entity(entity: HaEntitySummary) -> DiscoveredEntity:
         return _result(
             entity,
             EntityRole.ACTUATOR,
+            category=_actuator_category(entity),
             learnable=False,
             reason="Aktor ist ein mögliches Automationsziel, aber kein Trainingssensor.",
         )
@@ -138,6 +168,7 @@ def classify_entity(entity: HaEntitySummary) -> DiscoveredEntity:
     return _result(
         entity,
         EntityRole.UNSUPPORTED,
+        category="unsupported",
         learnable=False,
         reason="Entity-Typ ist noch nicht für Lernen oder Automationen klassifiziert.",
     )
@@ -162,6 +193,7 @@ def _result(
     entity: HaEntitySummary,
     role: EntityRole,
     *,
+    category: str,
     learnable: bool,
     reason: str,
 ) -> DiscoveredEntity:
@@ -171,7 +203,62 @@ def _result(
         device_class=entity.device_class,
         state_class=entity.state_class,
         unit_of_measurement=entity.unit_of_measurement,
+        category=category,
         role=role,
         learnable=learnable,
         reason=reason,
     )
+
+
+def _actuator_category(entity: HaEntitySummary) -> str:
+    if entity.domain == "light":
+        return "light"
+    if entity.domain == "switch":
+        return "switch_socket"
+    if entity.domain == "button" or entity.domain == "input_button":
+        return "button"
+    if entity.domain == "cover":
+        return "cover_shutter"
+    if entity.domain == "climate":
+        return "heating"
+    if entity.domain == "lock":
+        return "lock"
+    if entity.domain == "fan":
+        return "fan"
+    if entity.domain in {"input_boolean", "number"}:
+        return "helper"
+    return entity.domain
+
+
+def _measurement_category(entity: HaEntitySummary) -> str:
+    device_class = entity.device_class or ""
+    if device_class == "illuminance":
+        return "brightness"
+    if device_class == "temperature":
+        return "temperature"
+    if device_class in {"humidity", "moisture"}:
+        return "humidity"
+    if device_class in {"power", "energy", "current", "voltage"}:
+        return "energy_power"
+    if device_class in {"battery", "signal_strength"}:
+        return "diagnostic"
+    return "measurement"
+
+
+def _binary_category(entity: HaEntitySummary) -> str:
+    device_class = entity.device_class or ""
+    if device_class in {"motion", "occupancy", "presence"}:
+        return "presence_motion"
+    if device_class in {"door", "garage_door", "opening", "window"}:
+        return "opening"
+    if device_class in {"smoke", "safety", "problem"}:
+        return "safety"
+    return "binary"
+
+
+def _context_category(entity: HaEntitySummary) -> str:
+    if entity.domain.startswith("input_"):
+        return "helper"
+    if entity.domain in {"person", "device_tracker", "zone"}:
+        return "presence_location"
+    return entity.domain

app/main.py

@@ -102,7 +102,7 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
 app = FastAPI(
     title="SillyHome Next API",
     description="Lokales Smart-Home-Intelligenzsystem für Home Assistant.",
-    version="0.7.10",
+    version="0.7.11",
     lifespan=lifespan,
 )
 app.state.settings = load_settings()

app/static/index.html

@@ -134,9 +134,16 @@
           <option value="">Alle steuerbaren Typen</option>
           <option value="light">Lichter</option>
           <option value="switch">Schalter / Helper</option>
+          <option value="button">Buttons</option>
+          <option value="input_button">Helper-Buttons</option>
+          <option value="input_boolean">Helper-Schalter</option>
           <option value="cover">Rollläden / Cover</option>
+          <option value="climate">Heizungen / Klima</option>
+          <option value="lock">Schlösser</option>
           <option value="fan">Lüftung / Ventilatoren</option>
           <option value="humidifier">Befeuchter / Entfeuchter</option>
+          <option value="number">Numerische Helper</option>
+          <option value="valve">Ventile</option>
         </select>
       </div>
       <div>
@@ -319,11 +326,18 @@ async function loadActuatorDiscovery() {
 
 function actuatorGroupLabel(domain) {
   const labels = {
+    button: "Buttons",
+    climate: "Heizungen / Klima",
     light: "Lichter",
-    switch: "Schalter / Helper",
+    input_boolean: "Helper-Schalter",
+    input_button: "Helper-Buttons",
+    lock: "Schlösser",
+    number: "Numerische Helper",
+    switch: "Schalter / Steckdosen",
     cover: "Rollläden / Cover",
     fan: "Lüftung / Ventilatoren",
     humidifier: "Befeuchter / Entfeuchter",
+    valve: "Ventile",
   };
   return labels[domain] || domain;
 }
@@ -574,6 +588,10 @@ async function showActuator(actuatorId, evaluationMessage = "") {
       ${prediction
         ? `<p><strong>${escapeHtml(prediction.target_state)}</strong> mit ${Math.round(prediction.confidence * 100)} % Sicherheit. ${escapeHtml(prediction.reason)} <span class="${prediction.executed ? "ok" : "muted"}">${escapeHtml(prediction.execution_reason)}</span></p>`
         : "<p class='muted'>Aktuell ist kein gelerntes Handlungsmuster fällig.</p>"}
+      <div class="actions">
+        <button class="secondary" onclick="sendFeedback('${escapeHtml(record.actuator_entity_id)}', true)">Vorhersage korrekt</button>
+        <button class="secondary" onclick="sendFeedback('${escapeHtml(record.actuator_entity_id)}', false)">Vorhersage falsch</button>
+      </div>
       <h3>Passende Home-Assistant-Automationen</h3>
       <p class="muted">Bei einer Übernahme pausiert SillyHome diese Automationen. Beim Stoppen können sie gezielt fortgesetzt werden.</p>
       ${automationControls}
@@ -632,6 +650,23 @@ async function evaluateActuator(actuatorId) {
   }
 }
 
+async function sendFeedback(actuatorId, correct) {
+  const expectedState = correct ? null : prompt("Welcher Zustand wäre korrekt gewesen? Leer lassen, wenn nur abwerten.");
+  try {
+    await api(`v1/actuators/${encodeURIComponent(actuatorId)}/feedback`, {
+      method: "POST",
+      body: JSON.stringify({
+        correct,
+        expected_state: expectedState || null,
+      }),
+    });
+    await loadConfiguredActuators();
+    await showActuator(actuatorId, correct ? "Vorhersage als korrekt gelernt." : "Vorhersage als falsch markiert.");
+  } catch (error) {
+    alert(error.message);
+  }
+}
+
 async function setActivation(actuatorId, active, pauseMatchingAutomations, restorePausedAutomations) {
   const question = active
     ? pauseMatchingAutomations

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "sillyhome-next"
-version = "0.7.10"
+version = "0.7.11"
 description = "Lokales Smart-Home-Intelligenzsystem für Home Assistant"
 requires-python = ">=3.11"
 dependencies = [

tests/api/test_actuators.py

@@ -9,6 +9,7 @@ from app.actuators.lifecycle import ActuatorReconciliationService
 from app.actuators.store import ActuatorStore
 from app.behavior.engine import BehaviorEngine
 from app.config import Settings
+from app.api.v1.actuators import _deduplicate_actuator_ids
 from app.ha.discovery import DiscoveredEntity
 from app.ha.discovery import discover_entities
 from app.ha.history import (
@@ -227,3 +228,37 @@ def test_context_options_returns_learnable_entities(tmp_path: Path) -> None:
     assert "sensor.abstellkammer_illuminance" in entity_ids
     assert "binary_sensor.abstellkammer_motion" in entity_ids
     assert "sensor.pfsense_interface_vpn_inbytes" not in entity_ids
+
+
+def test_actuator_discovery_prefers_light_over_duplicate_switch() -> None:
+    entities = {
+        "light.schreibtisch": HaEntitySummary(
+            entity_id="light.schreibtisch",
+            domain="light",
+            friendly_name="Schreibtisch Licht",
+            device_id="device-1",
+        ),
+        "switch.schreibtisch": HaEntitySummary(
+            entity_id="switch.schreibtisch",
+            domain="switch",
+            friendly_name="Schreibtisch Schalter",
+            device_id="device-1",
+        ),
+        "cover.rollladen": HaEntitySummary(
+            entity_id="cover.rollladen",
+            domain="cover",
+            friendly_name="Rollladen",
+            device_id="device-2",
+        ),
+    }
+
+    result = _deduplicate_actuator_ids(
+        [
+            ("switch.schreibtisch", "switch_socket"),
+            ("light.schreibtisch", "light"),
+            ("cover.rollladen", "cover_shutter"),
+        ],
+        entities,
+    )
+
+    assert result == ["cover.rollladen", "light.schreibtisch"]

tests/api/test_entities.py

@@ -27,6 +27,7 @@ class FakeHaReader(HaReader):
             entity_id="sensor.temperature",
             domain="sensor",
             device_class="temperature",
+            category="temperature",
             role=EntityRole.MEASUREMENT,
             learnable=True,
             reason="Numerischer Messsensor für Zeitreihen und Training.",
@@ -116,6 +117,7 @@ def test_discovery_filters_entities() -> None:
             "device_class": "temperature",
             "state_class": None,
             "unit_of_measurement": None,
+            "category": "temperature",
             "role": "measurement",
             "learnable": True,
             "reason": "Numerischer Messsensor für Zeitreihen und Training.",

tests/behavior/test_engine.py

@@ -8,6 +8,7 @@ import pytest
 from app.actuators.models import (
     BehaviorMode,
     BehaviorPattern,
+    BehaviorPrediction,
     BehaviorState,
     BehaviorStatus,
     ExecutionEvent,
@@ -212,6 +213,125 @@ def test_engine_counts_known_automation_actions_like_manual_actions(
     assert {pattern.weight for pattern in trained.behavior.patterns} == {1.0}
 
 
+def test_feedback_marks_prediction_correct_as_learning_pattern(
+    tmp_path: Path,
+) -> None:
+    now = datetime.now(timezone.utc).replace(second=0, microsecond=0)
+    settings = _settings(tmp_path)
+    store = ActuatorStore(settings.actuator_store)
+    record = store.configure("light.office")
+    record = record.model_copy(
+        update={
+            "assignment": record.assignment.model_copy(
+                update={
+                    "selected_context_entity_ids": [
+                        "binary_sensor.office_presence"
+                    ],
+                }
+            ),
+            "behavior": record.behavior.model_copy(
+                update={
+                    "prediction": BehaviorPrediction(
+                        target_state="on",
+                        confidence=0.9,
+                        generated_at=now,
+                        reason="test",
+                    )
+                }
+            ),
+        }
+    )
+    store.upsert(record)
+    reader = FakeBehaviorReader(
+        entities=[
+            HaEntitySummary(entity_id="light.office", domain="light", state="off"),
+            HaEntitySummary(
+                entity_id="binary_sensor.office_presence",
+                domain="binary_sensor",
+                state="on",
+            ),
+        ],
+        history=[],
+        logbook=[],
+    )
+    engine = BehaviorEngine(ha_reader=reader, store=store, settings=settings)
+
+    result = engine.record_feedback("light.office", correct=True)
+
+    assert result.behavior.patterns[-1].target_state == "on"
+    assert result.behavior.patterns[-1].context_states == {
+        "binary_sensor.office_presence": "on"
+    }
+    assert result.behavior.patterns[-1].source == "user_feedback"
+    assert result.behavior.reason == "Vorhersage wurde vom Nutzer als korrekt bestätigt."
+
+
+def test_feedback_marks_prediction_wrong_and_adds_correction(
+    tmp_path: Path,
+) -> None:
+    now = datetime.now(timezone.utc).replace(second=0, microsecond=0)
+    settings = _settings(tmp_path)
+    store = ActuatorStore(settings.actuator_store)
+    record = store.configure("light.office")
+    record = record.model_copy(
+        update={
+            "assignment": record.assignment.model_copy(
+                update={
+                    "selected_context_entity_ids": [
+                        "binary_sensor.office_presence"
+                    ],
+                }
+            ),
+            "behavior": record.behavior.model_copy(
+                update={
+                    "patterns": [
+                        BehaviorPattern(
+                            target_state="on",
+                            minute_of_day=60,
+                            weekday=0,
+                            context_states={"binary_sensor.office_presence": "on"},
+                            source="automation",
+                            weight=1.0,
+                            observed_at=now - timedelta(days=1),
+                        )
+                    ],
+                    "prediction": BehaviorPrediction(
+                        target_state="on",
+                        confidence=0.9,
+                        generated_at=now,
+                        reason="test",
+                    ),
+                }
+            ),
+        }
+    )
+    store.upsert(record)
+    reader = FakeBehaviorReader(
+        entities=[
+            HaEntitySummary(entity_id="light.office", domain="light", state="off"),
+            HaEntitySummary(
+                entity_id="binary_sensor.office_presence",
+                domain="binary_sensor",
+                state="on",
+            ),
+        ],
+        history=[],
+        logbook=[],
+    )
+    engine = BehaviorEngine(ha_reader=reader, store=store, settings=settings)
+
+    result = engine.record_feedback(
+        "light.office",
+        correct=False,
+        expected_state="off",
+    )
+
+    assert result.behavior.patterns[0].weight == 0.1
+    assert result.behavior.patterns[-1].target_state == "off"
+    assert result.behavior.patterns[-1].source == "user_correction"
+    assert result.behavior.reason == "Vorhersage wurde vom Nutzer als falsch markiert."
+
+
 def test_engine_learns_causal_automation_with_activation_credit(
     tmp_path: Path,
 ) -> None:

tests/ha/test_discovery.py

@@ -74,3 +74,56 @@ def test_discovery_filters_domain_and_learnable() -> None:
     result = discover_entities(entities, domains={" SENSOR "}, learnable=True)
 
     assert [item.entity_id for item in result] == ["sensor.temperature"]
+
+
+@pytest.mark.parametrize(
+    ("entity", "category"),
+    [
+        (
+            HaEntitySummary(entity_id="climate.bad", domain="climate"),
+            "heating",
+        ),
+        (
+            HaEntitySummary(entity_id="lock.front_door", domain="lock"),
+            "lock",
+        ),
+        (
+            HaEntitySummary(entity_id="input_boolean.sleep_mode", domain="input_boolean"),
+            "helper",
+        ),
+        (
+            HaEntitySummary(
+                entity_id="sensor.brightness",
+                domain="sensor",
+                device_class="illuminance",
+            ),
+            "brightness",
+        ),
+        (
+            HaEntitySummary(
+                entity_id="binary_sensor.motion",
+                domain="binary_sensor",
+                device_class="motion",
+            ),
+            "presence_motion",
+        ),
+    ],
+)
+def test_classify_entity_categories(entity: HaEntitySummary, category: str) -> None:
+    assert classify_entity(entity).category == category
+
+
+@pytest.mark.parametrize(
+    "entity",
+    [
+        HaEntitySummary(entity_id="automation.lights", domain="automation"),
+        HaEntitySummary(entity_id="update.core", domain="update"),
+    ],
+)
+def test_classify_excludes_non_actuator_management_entities(
+    entity: HaEntitySummary,
+) -> None:
+    result = classify_entity(entity)
+
+    assert result.role is EntityRole.UNSUPPORTED
+    assert result.learnable is False